Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations2430251
Missing cells2615978
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory278.1 MiB
Average record size in memory120.0 B

Variable types

Numeric12
DateTime2
Categorical1

Alerts

arrival_delay_m is highly overall correlated with prev_arrival_delay_m and 2 other fieldsHigh correlation
max_station_number is highly overall correlated with stop_numberHigh correlation
prev_arrival_delay_m is highly overall correlated with arrival_delay_m and 2 other fieldsHigh correlation
prev_departure_delay_m is highly overall correlated with arrival_delay_m and 2 other fieldsHigh correlation
station_progress is highly overall correlated with stop_numberHigh correlation
stop_number is highly overall correlated with max_station_number and 1 other fieldsHigh correlation
weighted_avg_prev_delay is highly overall correlated with arrival_delay_m and 2 other fieldsHigh correlation
transformed_info_message is highly imbalanced (52.4%) Imbalance
IBNR has 121088 (5.0%) missing values Missing
arrival_plan has 831630 (34.2%) missing values Missing
departure_plan has 831630 (34.2%) missing values Missing
arrival_delay_m has 831630 (34.2%) missing values Missing
arrival_delay_m has 1043247 (42.9%) zeros Zeros
prev_arrival_delay_m has 1936581 (79.7%) zeros Zeros
prev_departure_delay_m has 1880371 (77.4%) zeros Zeros
weighted_avg_prev_delay has 1467334 (60.4%) zeros Zeros

Reproduction

Analysis started2024-11-19 22:54:30.821992
Analysis finished2024-11-19 22:56:56.855073
Duration2 minutes and 26.03 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

ID_Base
Real number (ℝ)

Distinct40191
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4653179 × 1016
Minimum-9.223177 × 1018
Maximum9.2217322 × 1018
Zeros0
Zeros (%)0.0%
Negative1222099
Negative (%)50.3%
Memory size18.5 MiB
2024-11-19T23:56:56.935206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.223177 × 1018
5-th percentile-8.3267611 × 1018
Q1-4.5909339 × 1018
median-5.8750657 × 1016
Q34.5603096 × 1018
95-th percentile8.3397438 × 1018
Maximum9.2217322 × 1018
Range-1.8348813 × 1015
Interquartile range (IQR)9.1512435 × 1018

Descriptive statistics

Standard deviation5.3244526 × 1018
Coefficient of variation (CV)-215.97428
Kurtosis-1.1923179
Mean-2.4653179 × 1016
Median Absolute Deviation (MAD)4.5753167 × 1018
Skewness0.010512774
Sum1.6107104 × 1018
Variance2.8349795 × 1037
MonotonicityNot monotonic
2024-11-19T23:56:57.087537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.256484864 × 1018413
 
< 0.1%
8.467202706 × 1018390
 
< 0.1%
8.668076605 × 1018382
 
< 0.1%
-7.996941865 × 1018381
 
< 0.1%
-2.094717035 × 1018345
 
< 0.1%
-1.78380972 × 1017338
 
< 0.1%
2.688663988 × 1018337
 
< 0.1%
-8.560851479 × 1018321
 
< 0.1%
-6.831600949 × 1018309
 
< 0.1%
-6.568589303 × 1018309
 
< 0.1%
Other values (40181) 2426726
99.9%
ValueCountFrequency (%)
-9.223176951 × 10185
 
< 0.1%
-9.222587614 × 101817
 
< 0.1%
-9.222235769 × 101842
 
< 0.1%
-9.221813993 × 1018202
< 0.1%
-9.221229322 × 10185
 
< 0.1%
-9.221103336 × 101891
< 0.1%
-9.220755073 × 101833
 
< 0.1%
-9.220659516 × 1018110
< 0.1%
-9.220172063 × 101820
 
< 0.1%
-9.219634608 × 101818
 
< 0.1%
ValueCountFrequency (%)
9.221732242 × 101881
< 0.1%
9.221055243 × 101853
 
< 0.1%
9.220892138 × 101815
 
< 0.1%
9.22087069 × 101854
 
< 0.1%
9.220854484 × 10187
 
< 0.1%
9.219893508 × 1018144
< 0.1%
9.219684671 × 10185
 
< 0.1%
9.219589171 × 101814
 
< 0.1%
9.218406789 × 101842
 
< 0.1%
9.218312429 × 101856
 
< 0.1%

ID_Timestamp
Real number (ℝ)

Distinct10109
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4071086 × 109
Minimum2.4070319 × 109
Maximum2.4071424 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:56:57.271276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.4070319 × 109
5-th percentile2.4070807 × 109
Q12.4070914 × 109
median2.4071109 × 109
Q32.4071222 × 109
95-th percentile2.4071415 × 109
Maximum2.4071424 × 109
Range110501
Interquartile range (IQR)30843

Descriptive statistics

Standard deviation21563.79
Coefficient of variation (CV)8.9583785 × 10-6
Kurtosis-0.02141794
Mean2.4071086 × 109
Median Absolute Deviation (MAD)19381
Skewness-0.3486081
Sum5.849878 × 1015
Variance4.6499703 × 108
MonotonicityNot monotonic
2024-11-19T23:56:57.425586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2407080833 835
 
< 0.1%
2407090833 835
 
< 0.1%
2407091633 823
 
< 0.1%
2407100833 814
 
< 0.1%
2407081633 809
 
< 0.1%
2407111633 799
 
< 0.1%
2407120733 796
 
< 0.1%
2407101633 795
 
< 0.1%
2407110833 792
 
< 0.1%
2407120833 789
 
< 0.1%
Other values (10099) 2422164
99.7%
ValueCountFrequency (%)
2407031857 3
 
< 0.1%
2407040236 24
 
< 0.1%
2407040245 11
 
< 0.1%
2407040253 2
 
< 0.1%
2407040302 19
 
< 0.1%
2407040303 6
 
< 0.1%
2407040312 20
 
< 0.1%
2407040313 30
< 0.1%
2407040314 1
 
< 0.1%
2407040317 65
< 0.1%
ValueCountFrequency (%)
2407142358 1
 
< 0.1%
2407142354 4
 
< 0.1%
2407142353 6
 
< 0.1%
2407142352 3
 
< 0.1%
2407142351 25
< 0.1%
2407142350 9
 
< 0.1%
2407142349 3
 
< 0.1%
2407142348 29
< 0.1%
2407142347 6
 
< 0.1%
2407142346 23
< 0.1%

stop_number
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.585509
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:56:57.954359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q315
95-th percentile25
Maximum59
Range58
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4487918
Coefficient of variation (CV)0.70367819
Kurtosis0.73941142
Mean10.585509
Median Absolute Deviation (MAD)5
Skewness1.0059828
Sum25725444
Variance55.4845
MonotonicityNot monotonic
2024-11-19T23:56:58.106894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 193286
 
8.0%
3 183914
 
7.6%
4 175289
 
7.2%
5 164351
 
6.8%
6 152967
 
6.3%
7 141799
 
5.8%
8 131737
 
5.4%
9 123448
 
5.1%
10 114022
 
4.7%
11 104747
 
4.3%
Other values (49) 944691
38.9%
ValueCountFrequency (%)
1 40018
 
1.6%
2 193286
8.0%
3 183914
7.6%
4 175289
7.2%
5 164351
6.8%
6 152967
6.3%
7 141799
5.8%
8 131737
5.4%
9 123448
5.1%
10 114022
4.7%
ValueCountFrequency (%)
59 33
 
< 0.1%
58 33
 
< 0.1%
57 33
 
< 0.1%
56 34
 
< 0.1%
55 31
 
< 0.1%
54 42
 
< 0.1%
53 60
< 0.1%
52 59
< 0.1%
51 73
< 0.1%
50 120
< 0.1%

IBNR
Real number (ℝ)

Missing 

Distinct5198
Distinct (%)0.2%
Missing121088
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean8020144.7
Minimum8000001
Maximum8099506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:56:58.235429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8000001
5-th percentile8000208
Q18002101
median8004483
Q38011755
95-th percentile8089091
Maximum8099506
Range99505
Interquartile range (IQR)9654

Descriptive statistics

Standard deviation32816.948
Coefficient of variation (CV)0.0040918149
Kurtosis0.59370912
Mean8020144.7
Median Absolute Deviation (MAD)2937
Skewness1.5822273
Sum1.8519821 × 1013
Variance1.0769521 × 109
MonotonicityNot monotonic
2024-11-19T23:56:58.359274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8089028 9069
 
0.4%
8004128 8618
 
0.4%
8098549 7543
 
0.3%
8004129 7432
 
0.3%
8004135 7432
 
0.3%
8004131 7425
 
0.3%
8004132 7424
 
0.3%
8089047 7250
 
0.3%
8004136 7121
 
0.3%
8004179 6589
 
0.3%
Other values (5188) 2233260
91.9%
(Missing) 121088
 
5.0%
ValueCountFrequency (%)
8000001 490
< 0.1%
8000002 1
 
< 0.1%
8000004 347
 
< 0.1%
8000007 347
 
< 0.1%
8000009 455
< 0.1%
8000010 365
 
< 0.1%
8000011 570
< 0.1%
8000012 429
< 0.1%
8000013 977
< 0.1%
8000014 427
< 0.1%
ValueCountFrequency (%)
8099506 197
 
< 0.1%
8098553 4453
0.2%
8098549 7543
0.3%
8098360 1
 
< 0.1%
8098348 192
 
< 0.1%
8098263 6323
0.3%
8098205 2651
 
0.1%
8098193 462
 
< 0.1%
8098147 2544
 
0.1%
8098105 4921
0.2%

long
Real number (ℝ)

Distinct3125
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.171016
Minimum0.834032
Maximum14.982644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:56:58.487530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.834032
5-th percentile6.852636
Q18.364945
median9.918336
Q312.201874
95-th percentile13.553202
Maximum14.982644
Range14.148612
Interquartile range (IQR)3.836929

Descriptive statistics

Standard deviation2.3114801
Coefficient of variation (CV)0.22726147
Kurtosis-1.1159849
Mean10.171016
Median Absolute Deviation (MAD)1.777579
Skewness0.11707699
Sum24718122
Variance5.3429402
MonotonicityNot monotonic
2024-11-19T23:56:58.626523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.536537 8022
 
0.3%
11.575386 7373
 
0.3%
11.583234 7368
 
0.3%
11.548572 7363
 
0.3%
11.565619 7329
 
0.3%
13.283966 7075
 
0.3%
11.593049 6923
 
0.3%
11.519245 6512
 
0.3%
11.503669 6498
 
0.3%
11.604971 6132
 
0.3%
Other values (3115) 2359656
97.1%
ValueCountFrequency (%)
0.834032 725
< 0.1%
0.896632 710
< 0.1%
6.070715 1427
0.1%
6.07384 894
< 0.1%
6.074485 1049
< 0.1%
6.08378 724
< 0.1%
6.091499 441
 
< 0.1%
6.094486 1279
0.1%
6.097265 807
< 0.1%
6.098877 719
< 0.1%
ValueCountFrequency (%)
14.982644 758
< 0.1%
14.97908 189
 
< 0.1%
14.936008 1
 
< 0.1%
14.930408 727
< 0.1%
14.902088 248
 
< 0.1%
14.889318 738
< 0.1%
14.825531 738
< 0.1%
14.825234 738
< 0.1%
14.805774 41
 
< 0.1%
14.706775 259
 
< 0.1%

lat
Real number (ℝ)

Distinct3130
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.965929
Minimum47.417954
Maximum55.021381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:56:58.808735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum47.417954
5-th percentile48.047217
Q149.382114
median51.047991
Q352.500737
95-th percentile53.965491
Maximum55.021381
Range7.6034266
Interquartile range (IQR)3.1186226

Descriptive statistics

Standard deviation1.91654
Coefficient of variation (CV)0.037604338
Kurtosis-0.96141838
Mean50.965929
Median Absolute Deviation (MAD)1.4719641
Skewness-0.00072081555
Sum1.2386 × 108
Variance3.6731256
MonotonicityNot monotonic
2024-11-19T23:56:58.999340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.142623 8022
 
0.3%
48.137048 7373
 
0.3%
48.134202 7368
 
0.3%
48.141969 7363
 
0.3%
48.139452 7329
 
0.3%
52.500737 7075
 
0.3%
48.129168 6923
 
0.3%
48.14354 6512
 
0.3%
48.144371 6498
 
0.3%
48.12744 6132
 
0.3%
Other values (3120) 2359656
97.1%
ValueCountFrequency (%)
47.4179544 718
< 0.1%
47.456591 213
 
< 0.1%
47.5058367 1496
0.1%
47.513241 428
 
< 0.1%
47.5251713 730
< 0.1%
47.543785 723
< 0.1%
47.544341 49
 
< 0.1%
47.547219 723
< 0.1%
47.54792 748
< 0.1%
47.549143 729
< 0.1%
ValueCountFrequency (%)
55.021381 749
< 0.1%
55.019862 751
< 0.1%
55.017947 733
< 0.1%
55.01765 736
< 0.1%
55.0149 725
< 0.1%
55.012455 744
< 0.1%
55.010432 765
< 0.1%
55.008077 731
< 0.1%
55.001937 697
< 0.1%
54.988543 753
< 0.1%

arrival_plan
Date

Missing 

Distinct10081
Distinct (%)0.6%
Missing831630
Missing (%)34.2%
Memory size18.5 MiB
Minimum2024-07-07 23:37:00
Maximum2024-07-14 23:58:00
2024-11-19T23:56:59.137718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:59.272385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

departure_plan
Date

Missing 

Distinct10077
Distinct (%)0.6%
Missing831630
Missing (%)34.2%
Memory size18.5 MiB
Minimum2024-07-08 00:00:00
Maximum2024-07-14 23:58:00
2024-11-19T23:56:59.396987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:59.517855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

arrival_delay_m
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct110
Distinct (%)< 0.1%
Missing831630
Missing (%)34.2%
Infinite0
Infinite (%)0.0%
Mean1.2553144
Minimum0
Maximum159
Zeros1043247
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:56:59.630203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum159
Range159
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.4423568
Coefficient of variation (CV)2.7422268
Kurtosis99.038054
Mean1.2553144
Median Absolute Deviation (MAD)0
Skewness7.386492
Sum2006772
Variance11.849821
MonotonicityNot monotonic
2024-11-19T23:56:59.746198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1043247
42.9%
1 218086
 
9.0%
2 112971
 
4.6%
3 69359
 
2.9%
4 38702
 
1.6%
5 26169
 
1.1%
6 18020
 
0.7%
7 12762
 
0.5%
8 10065
 
0.4%
9 8115
 
0.3%
Other values (100) 41125
 
1.7%
(Missing) 831630
34.2%
ValueCountFrequency (%)
0 1043247
42.9%
1 218086
 
9.0%
2 112971
 
4.6%
3 69359
 
2.9%
4 38702
 
1.6%
5 26169
 
1.1%
6 18020
 
0.7%
7 12762
 
0.5%
8 10065
 
0.4%
9 8115
 
0.3%
ValueCountFrequency (%)
159 1
 
< 0.1%
157 1
 
< 0.1%
140 1
 
< 0.1%
136 1
 
< 0.1%
134 1
 
< 0.1%
133 2
 
< 0.1%
120 1
 
< 0.1%
117 1
 
< 0.1%
116 1
 
< 0.1%
110 7
< 0.1%

transformed_info_message
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 MiB
No message
1895754 
Information
266395 
Bauarbeiten
 
140053
Störung
 
121627
Großstörung
 
6422

Length

Max length11
Median length10
Mean length10.019747
Min length7

Characters and Unicode

Total characters24350499
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo message
2nd rowNo message
3rd rowNo message
4th rowNo message
5th rowNo message

Common Values

ValueCountFrequency (%)
No message 1895754
78.0%
Information 266395
 
11.0%
Bauarbeiten 140053
 
5.8%
Störung 121627
 
5.0%
Großstörung 6422
 
0.3%

Length

2024-11-19T23:56:59.855841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T23:56:59.926426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 1895754
43.8%
message 1895754
43.8%
information 266395
 
6.2%
bauarbeiten 140053
 
3.2%
störung 121627
 
2.8%
großstörung 6422
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 4071614
16.7%
s 3797930
15.6%
a 2442255
10.0%
o 2434966
10.0%
m 2162149
8.9%
g 2023803
8.3%
N 1895754
7.8%
1895754
7.8%
n 800892
 
3.3%
r 540919
 
2.2%
Other values (11) 2284463
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24350499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4071614
16.7%
s 3797930
15.6%
a 2442255
10.0%
o 2434966
10.0%
m 2162149
8.9%
g 2023803
8.3%
N 1895754
7.8%
1895754
7.8%
n 800892
 
3.3%
r 540919
 
2.2%
Other values (11) 2284463
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24350499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4071614
16.7%
s 3797930
15.6%
a 2442255
10.0%
o 2434966
10.0%
m 2162149
8.9%
g 2023803
8.3%
N 1895754
7.8%
1895754
7.8%
n 800892
 
3.3%
r 540919
 
2.2%
Other values (11) 2284463
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24350499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4071614
16.7%
s 3797930
15.6%
a 2442255
10.0%
o 2434966
10.0%
m 2162149
8.9%
g 2023803
8.3%
N 1895754
7.8%
1895754
7.8%
n 800892
 
3.3%
r 540919
 
2.2%
Other values (11) 2284463
9.4%

prev_arrival_delay_m
Real number (ℝ)

High correlation  Zeros 

Distinct103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72297676
Minimum0
Maximum159
Zeros1936581
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:57:00.005386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum159
Range159
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6519985
Coefficient of variation (CV)3.6681656
Kurtosis159.62441
Mean0.72297676
Median Absolute Deviation (MAD)0
Skewness9.4458696
Sum1757015
Variance7.0330962
MonotonicityNot monotonic
2024-11-19T23:57:00.099549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1936581
79.7%
1 194073
 
8.0%
2 101566
 
4.2%
3 62576
 
2.6%
4 34381
 
1.4%
5 23066
 
0.9%
6 15828
 
0.7%
7 11062
 
0.5%
8 8783
 
0.4%
9 7055
 
0.3%
Other values (93) 35280
 
1.5%
ValueCountFrequency (%)
0 1936581
79.7%
1 194073
 
8.0%
2 101566
 
4.2%
3 62576
 
2.6%
4 34381
 
1.4%
5 23066
 
0.9%
6 15828
 
0.7%
7 11062
 
0.5%
8 8783
 
0.4%
9 7055
 
0.3%
ValueCountFrequency (%)
159 1
 
< 0.1%
140 1
 
< 0.1%
136 1
 
< 0.1%
134 1
 
< 0.1%
133 1
 
< 0.1%
120 1
 
< 0.1%
110 7
< 0.1%
109 2
 
< 0.1%
107 2
 
< 0.1%
106 2
 
< 0.1%

prev_departure_delay_m
Real number (ℝ)

High correlation  Zeros 

Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76171926
Minimum0
Maximum159
Zeros1880371
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:57:00.187963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum159
Range159
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.682931
Coefficient of variation (CV)3.5222045
Kurtosis156.02847
Mean0.76171926
Median Absolute Deviation (MAD)0
Skewness9.3293082
Sum1851169
Variance7.1981187
MonotonicityNot monotonic
2024-11-19T23:57:00.280524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1880371
77.4%
1 233223
 
9.6%
2 115673
 
4.8%
3 63053
 
2.6%
4 35162
 
1.4%
5 23343
 
1.0%
6 15831
 
0.7%
7 11250
 
0.5%
8 8895
 
0.4%
9 7074
 
0.3%
Other values (95) 36376
 
1.5%
ValueCountFrequency (%)
0 1880371
77.4%
1 233223
 
9.6%
2 115673
 
4.8%
3 63053
 
2.6%
4 35162
 
1.4%
5 23343
 
1.0%
6 15831
 
0.7%
7 11250
 
0.5%
8 8895
 
0.4%
9 7074
 
0.3%
ValueCountFrequency (%)
159 1
 
< 0.1%
137 1
 
< 0.1%
135 1
 
< 0.1%
134 2
 
< 0.1%
132 1
 
< 0.1%
120 1
 
< 0.1%
110 7
< 0.1%
109 1
 
< 0.1%
108 1
 
< 0.1%
107 1
 
< 0.1%

weighted_avg_prev_delay
Real number (ℝ)

High correlation  Zeros 

Distinct44253
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55055954
Minimum0
Maximum114.66667
Zeros1467334
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:57:00.371196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.35
95-th percentile2.6609848
Maximum114.66667
Range114.66667
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation1.8047582
Coefficient of variation (CV)3.2780436
Kurtosis180.35457
Mean0.55055954
Median Absolute Deviation (MAD)0
Skewness9.9561236
Sum1337997.9
Variance3.2571522
MonotonicityNot monotonic
2024-11-19T23:57:00.457094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1467334
60.4%
0.3333333333 15538
 
0.6%
0.6666666667 12848
 
0.5%
0.2 10604
 
0.4%
0.5 9779
 
0.4%
0.4 9360
 
0.4%
0.2857142857 7460
 
0.3%
0.25 6667
 
0.3%
1 6655
 
0.3%
0.1428571429 6276
 
0.3%
Other values (44243) 877730
36.1%
ValueCountFrequency (%)
0 1467334
60.4%
0.002844950213 4
 
< 0.1%
0.002898550725 1
 
< 0.1%
0.003003003003 16
 
< 0.1%
0.00303030303 1
 
< 0.1%
0.003171247357 1
 
< 0.1%
0.003174603175 17
 
< 0.1%
0.003322259136 1
 
< 0.1%
0.003361344538 17
 
< 0.1%
0.003484320557 1
 
< 0.1%
ValueCountFrequency (%)
114.6666667 1
< 0.1%
110.0714286 1
< 0.1%
93.76190476 1
< 0.1%
93.33333333 1
< 0.1%
84.61538462 1
< 0.1%
80 1
< 0.1%
78.06666667 1
< 0.1%
77.19047619 1
< 0.1%
74 1
< 0.1%
72.52747253 1
< 0.1%

max_station_number
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.355618
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:57:00.543316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median19
Q326
95-th percentile33
Maximum59
Range58
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.944886
Coefficient of variation (CV)0.46213385
Kurtosis-0.27620609
Mean19.355618
Median Absolute Deviation (MAD)7
Skewness0.25123229
Sum47039009
Variance80.010986
MonotonicityNot monotonic
2024-11-19T23:57:00.634190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 151807
 
6.2%
28 133181
 
5.5%
11 115516
 
4.8%
19 105387
 
4.3%
15 102738
 
4.2%
27 100765
 
4.1%
26 96620
 
4.0%
12 86910
 
3.6%
13 84562
 
3.5%
10 84223
 
3.5%
Other values (42) 1368542
56.3%
ValueCountFrequency (%)
1 1404
 
0.1%
2 10742
 
0.4%
3 18536
 
0.8%
4 35893
1.5%
5 50933
2.1%
6 60169
2.5%
7 57799
2.4%
8 63896
2.6%
9 75768
3.1%
10 84223
3.5%
ValueCountFrequency (%)
59 1916
 
0.1%
54 451
 
< 0.1%
53 845
 
< 0.1%
51 562
 
< 0.1%
50 2807
 
0.1%
49 41
 
< 0.1%
46 304
 
< 0.1%
45 538
 
< 0.1%
44 3994
0.2%
43 8302
0.3%

station_progress
Real number (ℝ)

High correlation 

Distinct847
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56476904
Minimum0.016949153
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.5 MiB
2024-11-19T23:57:00.727151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.016949153
5-th percentile0.125
Q10.33333333
median0.57142857
Q30.8
95-th percentile1
Maximum1
Range0.98305085
Interquartile range (IQR)0.46666667

Descriptive statistics

Standard deviation0.27742442
Coefficient of variation (CV)0.49121748
Kurtosis-1.1682545
Mean0.56476904
Median Absolute Deviation (MAD)0.23809524
Skewness-0.048710415
Sum1372530.5
Variance0.076964311
MonotonicityNot monotonic
2024-11-19T23:57:00.969930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 196985
 
8.1%
0.5 89285
 
3.7%
0.6666666667 63941
 
2.6%
0.3333333333 57055
 
2.3%
0.75 46247
 
1.9%
0.8 41700
 
1.7%
0.6 41351
 
1.7%
0.4 41266
 
1.7%
0.25 37494
 
1.5%
0.2 31234
 
1.3%
Other values (837) 1783693
73.4%
ValueCountFrequency (%)
0.01694915254 31
 
< 0.1%
0.01886792453 2
 
< 0.1%
0.02 1
 
< 0.1%
0.02173913043 1
 
< 0.1%
0.02222222222 10
 
< 0.1%
0.02272727273 51
< 0.1%
0.02325581395 14
 
< 0.1%
0.02380952381 92
< 0.1%
0.0243902439 23
 
< 0.1%
0.025 3
 
< 0.1%
ValueCountFrequency (%)
1 196985
8.1%
0.9830508475 33
 
< 0.1%
0.9814814815 9
 
< 0.1%
0.9811320755 17
 
< 0.1%
0.9803921569 12
 
< 0.1%
0.98 65
 
< 0.1%
0.9795918367 1
 
< 0.1%
0.9782608696 6
 
< 0.1%
0.9777777778 11
 
< 0.1%
0.9772727273 90
 
< 0.1%

Interactions

2024-11-19T23:56:41.889903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:37.839151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:44.638998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:50.166892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:55.885559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:02.442742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:08.664506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:14.562536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:17.518506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:24.494018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:31.422830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:36.363140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:42.363534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:38.584429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:45.078094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:50.665817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:56.402039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:02.748629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:09.235495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:14.796333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:17.848645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:24.999770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:31.837460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:36.818385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:42.810043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:39.357721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:45.526895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:51.146052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:56.904985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:03.050488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:09.919507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:15.030070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:18.423009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:25.450224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:32.236969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:37.405954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:43.274487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:40.039155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:45.992290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:51.633860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:57.586020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:03.726272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:10.503249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:15.244096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:19.003878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:25.929241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:32.649396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:37.919158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:43.750941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:40.565874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:46.441617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:52.112103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:58.199585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:04.466658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:11.183980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:15.468433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:19.521876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:26.513182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:33.050657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:38.409850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:44.206387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:41.138489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:46.931188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:52.600076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:58.951409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:05.136028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:11.726243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:15.717934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:20.053311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:27.106024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:33.455819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:38.861667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:44.531976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:41.864246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:47.266540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:52.935130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:59.440855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:05.496037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:12.110720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:15.982659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:20.421437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:27.509713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:33.752361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:39.189784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:44.979305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:42.391176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:47.729427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:53.377003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:00.032918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:06.116659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:12.648328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:16.253371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:20.926519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:28.230646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:34.168473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:39.627434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:45.429347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:42.837178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:48.194988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:53.824605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:00.580022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:06.704406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:13.197297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:16.531464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:21.415532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:28.975665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:34.616361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:40.082336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:45.879280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:43.291559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:48.702290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:54.387961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:01.152537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:07.090137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:13.711890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:16.764785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:22.262579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:29.544924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:35.034618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:40.525531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:46.378767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:43.733137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:49.196653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:54.901335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:01.702325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:07.547382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:14.020859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:16.997034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:22.977452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:30.146712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:35.484240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:40.968488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:46.849605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:44.193060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:49.679148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:55:55.384887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:02.145038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:08.022596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:14.334352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:17.213833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:23.832034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:30.957129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:35.931192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T23:56:41.401242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-19T23:57:01.039200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
IBNRID_BaseID_Timestamparrival_delay_mlatlongmax_station_numberprev_arrival_delay_mprev_departure_delay_mstation_progressstop_numbertransformed_info_messageweighted_avg_prev_delay
IBNR1.000-0.0030.001-0.1580.2480.4650.178-0.119-0.121-0.0340.1010.154-0.107
ID_Base-0.0031.000-0.001-0.0010.0020.003-0.0040.0000.0010.000-0.0020.0140.000
ID_Timestamp0.001-0.0011.000-0.0260.0040.0030.006-0.014-0.014-0.0000.0030.041-0.015
arrival_delay_m-0.158-0.001-0.0261.000-0.296-0.1330.1290.6530.6700.1510.2290.0090.630
lat0.2480.0020.004-0.2961.0000.214-0.003-0.191-0.202-0.012-0.0130.212-0.194
long0.4650.0030.003-0.1330.2141.0000.093-0.083-0.086-0.0160.0490.207-0.074
max_station_number0.178-0.0040.0060.129-0.0030.0931.0000.1760.155-0.1380.5720.1250.280
prev_arrival_delay_m-0.1190.000-0.0140.653-0.191-0.0830.1761.0000.8260.1600.2690.0130.731
prev_departure_delay_m-0.1210.001-0.0140.670-0.202-0.0860.1550.8261.0000.1410.2360.0120.650
station_progress-0.0340.000-0.0000.151-0.012-0.016-0.1380.1600.1411.0000.6610.0180.315
stop_number0.101-0.0020.0030.229-0.0130.0490.5720.2690.2360.6611.0000.0650.477
transformed_info_message0.1540.0140.0410.0090.2120.2070.1250.0130.0120.0180.0651.0000.013
weighted_avg_prev_delay-0.1070.000-0.0150.630-0.194-0.0740.2800.7310.6500.3150.4770.0131.000

Missing values

2024-11-19T23:56:47.204557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T23:56:49.340752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-19T23:56:53.540559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID_BaseID_Timestampstop_numberIBNRlonglatarrival_plandeparture_planarrival_delay_mtransformed_info_messageprev_arrival_delay_mprev_departure_delay_mweighted_avg_prev_delaymax_station_numberstation_progress
0-1001326572688500578240708204128011118.013.37598852.5093792024-07-08 20:44:002024-07-08 20:45:000.0No message0.00.00.00000070.285714
1-1001326572688500578240708204138011160.09.09585148.849792NaNNaNNaNNo message0.00.00.00000070.428571
2-1001326572688500578240708204148011167.013.29943752.5302762024-07-08 20:55:002024-07-08 20:56:000.0No message0.00.00.00000070.571429
3-1001326572688500578240708204158010404.013.19689852.5346482024-07-08 21:00:002024-07-08 21:03:002.0No message0.00.00.00000070.714286
4-1001326572688500578240708204168080040.013.12891752.5493962024-07-08 21:06:002024-07-08 21:07:001.0No message2.00.00.66666770.857143
5-1001326572688500578240708204178081586.013.11681052.5524802024-07-08 21:08:002024-07-08 21:09:006.0No message1.01.00.76190571.000000
6-1001326572688500578240709204128011118.013.37598852.5093792024-07-09 20:44:002024-07-09 20:45:000.0No message0.00.00.00000070.285714
7-1001326572688500578240709204138011160.08.30997054.920783NaNNaNNaNNo message0.00.00.00000070.428571
8-1001326572688500578240709204148011167.013.29943752.5302762024-07-09 20:55:002024-07-09 20:56:000.0No message0.00.00.00000070.571429
9-1001326572688500578240709204158010404.013.19689852.5346482024-07-09 21:00:002024-07-09 21:03:004.0No message0.00.00.00000070.714286
ID_BaseID_Timestampstop_numberIBNRlonglatarrival_plandeparture_planarrival_delay_mtransformed_info_messageprev_arrival_delay_mprev_departure_delay_mweighted_avg_prev_delaymax_station_numberstation_progress
2430241999976718847540977240709044768005649.07.11081449.2747632024-07-09 05:01:002024-07-09 05:02:001.0No message0.00.00.061.000000
2430242999976718847540977240710044728005241.07.01878849.2304252024-07-10 04:50:002024-07-10 04:51:000.0No message0.00.00.060.333333
2430243999976718847540977240710044738005306.07.19962251.177270NaNNaNNaNNo message0.00.00.060.500000
2430244999976718847540977240710044748005332.07.05708349.2440182024-07-10 04:55:002024-07-10 04:56:000.0No message0.00.00.060.666667
2430245999976718847540977240710044758005044.07.00424151.160909NaNNaNNaNNo message0.00.00.060.833333
2430246999976718847540977240710044768005649.07.11081449.2747632024-07-10 05:01:002024-07-10 05:02:001.0No message0.00.00.061.000000
2430247999976718847540977240712044728005241.07.01878849.2304252024-07-12 04:50:002024-07-12 04:51:000.0No message0.00.00.060.333333
2430248999976718847540977240712044738005306.08.24372850.070788NaNNaNNaNNo message0.00.00.060.500000
2430249999976718847540977240712044748005332.07.05708349.2440182024-07-12 04:55:002024-07-12 04:56:000.0No message0.00.00.060.666667
2430250999976718847540977240712044768005649.07.11081449.2747632024-07-12 05:01:002024-07-12 05:02:005.0No message0.00.00.061.000000